library(pitchRx)
# returns a list of related tables (see diagram below)
dat <- scrape(start = "2008-01-01", end = Sys.Date())

db <- dplyr::src_sqlite("pitchRx.sqlite3", create = TRUE)
pitchRx::scrape(start = "2008-01-01", end = Sys.Date(), connect = db$con)

• Any database connection should work!
• Writes data in streaming chunks to avoid exhausting memory.
• Keeping your database up-to-date is also easy!

update_db(db$con)

• Add other data!
  • openWAR estimates (open source calculation of Wins Above Replacement)
  • Exit Velocity and Distance of Batted Balls

source

Player/date info recorded on the at-bat level.

library(dplyr)
atbats <- tbl(db, 'atbat') %>%
  filter(pitcher_name == 'Yu Darvish', batter_name == 'Albert Pujols', 
         date == '2013_04_24')

• Now, obtain PITCHf/x data for these at-bats.

tbl(db, 'pitch') %>%
  inner_join(atbats, by = c('num', 'gameday_link')) %>%
  collect() %>% pitchRx::animateFX()

# condition on umpire decisions
pitches <- tbl(db, "pitch") %>%
  filter(des %in% c("Called Strike", "Ball")) %>%
  mutate(strike = as.numeric(des == "Called Strike"))

# goal is to compare 2008 to 2014
atbats <- tbl(db, "atbat") %>%
  mutate(year = substr(date, 5L, -4L)) %>%
  filter(year %in% c("2008", "2014"))
dat <- left_join(pitches, atbats)

library(mgcv)
# 48 (2 x 2 x 12) surfaces!
m <- bam(strike ~ interaction(stand, year, count) +
            s(px, pz, by = interaction(stand, year, count)),
          data = dat, family = binomial(link = 'logit'))

strikeFX(dat, model = m, density1 = list(year = "2008"),
          density2 = list(year = "2014"), 
          layer = facet_grid(count ~ stand))

• Called strikes at the knees were 2-4 times more likely in 2014 compared to 2008.

but called strikes up-and-in or up-and-away are much less likely nowadays.

• Form approximate CI on scale of the predictor, then transform to response scale.
  • Pros: computationally cheap
  • Cons: point-wise; approximate; assumes smoothness parameters are known
• Simulate from the posterior and obtain percentiles
  • Pros: simultaneous (as opposed to point-wise)
  • Cons: assumes smoothness parameters are known
• Parametric bootstrap
  • Pros: simultaneous (as opposed to point-wise); not conditional on smoothness parameters

• How do we test for a significant overall difference between scenario A and B?
• Possible solution: Visual Inference